The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Programming Boosting via Column Generation
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Techniques for Automated Architectural Reconstruction from Photographs
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
3D City Modeling Using Cognitive Loops
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Conservative visual learning for object detection with minimal hand labeling effort
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Ortho-image analysis for producing lane-level highway maps
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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The automatic creation of 3D models of urban spaces has become a very active field of research. This has been inspired by recent applications in the location-awareness on the Internet, as demonstrated in maps.live.com and similar websites. The level of automation in creating 3D city models has increased considerably, and has benefited from an increase in the redundancy of the source imagery, namely digital aerial photography. In this paper we argue that the next big step forward is to replace photographic texture by an interpretation of what the texture describes, and to achieve this fully automatically. One calls the result "semantic knowledge". For example we want to know that a certain part of the image is a car, a person, a building, a tree, a shrub, a window, a door, instead of just a collection of 3D points or triangles with a superimposed photographic texture. We investigate object recognition methods to make this next big step. We demonstrate an early result of using the on-line variant of a Boosting algorithm to indeed detect cars in aerial digital imagery to a satisfactory and useful level of completeness. And we show that we can use this semantic knowledge to produce improved orthophotos. We expect that also the 3D models will be improved by the knowledge of cars.